Estimation of Individual Tree Health Condition for Japanese Mountain Cherry (Cerasus jamasakura) Using Airborne LiDAR

Abstract

This study examined the usefulness of airborne light detection and ranging (LiDAR) data for estimating the individual tree health condition in Japanese mountain cherry (Cerasus jamasakura) in Yoshinoyama, Nara Prefecture, Japan. LiDAR variables that represented the ratio of lasers hitting tree components were calculated and their effectiveness was examined by relating them to the results of conventional field-based visual tree health assessments based on ordination, correlation analyses, and generalized linear models. The results showed that many of the LiDAR variables had significant correlations with the variables derived from visually evaluated tree health condition. In particular, the proportion of “only” returns, which represents the ratio of the lasers reflected from the crown surfaces, was the most effective for estimating total health condition in relation to the crown density, one of the key health indicators for representing physical properties. The individuals with large estimation errors had smaller crowns than the individuals with small errors, suggesting that sufficiently large crown sizes are important for more accurate estimations of the tree health condition using airborne LiDAR data.